Mapping the coverage of attributes in validated instruments that evaluate primary healthcare from the patient perspective
Bibliographic record
Abstract
BACKGROUND: Primary healthcare in developed countries is undergoing important reforms, and these require evaluation strategies to assess how well the population's expectations are being met. Although numerous instruments are available to evaluate primary healthcare (PHC) from the patient perspective, they do not all measure the same range of constructs. To analyze the extent to which important PHC attributes are covered in validated instruments measuring quality of care from the patient perspective. METHOD: We systematically identified validated instruments from the literature and by consulting experts. Using a Delphi consensus-building process, Canadian PHC experts identified and operationally defined 24 important PHC attributes. One team member mapped instrument subscales to these operational definitions; this mapping was then independently validated by members of the research team and conflicts were resolved by the PHC experts. RESULTS: Of the 24 operational definitions, 13 were evaluated as being best measured by patients, 10 by providers, three by administrative databases and one by chart audits (some being best measured by more than one source). Our search retained 17 measurement tools containing 118 subscales. After eliminating redundancies, we mapped 13 unique measurement tools to the PHC attributes. Accessibility, relational continuity, interpersonal communication, management continuity, respectfulness and technical quality of clinical care were the attributes widely covered by available instruments. Advocacy, management of clinical information, comprehensiveness of services, cultural sensitivity, family-centred care, whole-person care and equity were poorly covered. CONCLUSIONS: Validated instruments to evaluate PHC quality from the patient perspective leave many important attributes of PHC uncovered. A complete assessment of PHC quality will require adjusting existing tools and/or developing new instruments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".